Over the past decade, tremendous amount of research activity has focused around the problem of localization in GPS denied environments. Challenges with localization are highlighted in human wearable systems where the operator can freely move through both indoors and outdoors. In this paper, we present a robust method that addresses these challenges using a human wearable system with two pairs of backward and forward looking stereo cameras together with an inertial measurement unit (IMU). This algorithm can run in real-time with 15 Hz update rate on a dual-core 2 GHz laptop PC and it is designed to be a highly accurate local (relative) pose estimation mechanism acting as the front-end to a simultaneous localization and mapping (SLAM) type method capable of global corrections through landmark matching. Extensive tests of our prototype system so far, reveal that without any global landmark matching, we achieve between 0.5% and 1% accuracy in localizing a person over a 500 meter travel indoors and outdoors. To our knowledge, such performance results with a real time system have not been reported before.
Supun Samarasekera
SRI Author
On-The-Move Independently Moving Target Detection
This paper describes a system for automatically detecting potential targets (that pop-up or move into view) and to cue the operator to potential threats. Detection of independently moving targets from a moving ground vehicle is challenging due to the strong parallax effects caused by the camera motion close to the 3D structure in the environment. We present a 3D approach for detecting and tracking such independently moving targets with multiple monocular cameras. In our approach, we first recover the camera position and orientation by employing a visual odometry method. Next, using multiple consecutive frames with the estimated camera poses, the structure of the scene at the reference frame is explicitly recovered by a motion stereo approach, and corresponding optical flow fields between the reference frame and other frames are also estimated. Third, an advanced filter is designed by combining second order differences between 3D warping and optical flow warping to distinguish the moving object from parallax regions. We present results of the algorithm on data collected with an eight-camera system mounted on a vehicle under multiple scenarios that include moving and pop-up targets.
Precise Visual Navigation Using Multi-Stereo Vision and Landmark Matching
Traditional vision-based navigation system often drifts over time during navigation. In this paper, we propose a set of techniques which greatly reduce the long term drift and also improve its robustness to many failure conditions. In our approach, two pairs of stereo cameras are integrated to form a forward/backward multi-stereo camera system. As a result, the Field-Of-View of the system is extended significantly to capture more natural landmarks from the scene. This helps to increase the pose estimation accuracy as well as reduce the failure situations. Secondly, a global landmark matching technique is used to recognize the previously visited locations during navigation. Using the matched landmarks, a pose correction technique is used to eliminate the accumulated navigation drift. Finally, in order to further improve the robustness of the system, measurements from low-cost Inertial Measurement Unit (IMU) and Global Positioning System (GPS) sensors are integrated with the visual odometry in an extended Kalman Filtering framework. Our system is significantly more accurate and robust than previously published techniques (1∼5% localization error) over long-distance navigation both indoors and outdoors. Real world experiments on a human worn system show that the location can be estimated within 1 meter over 500 meters (around 0.1% localization error averagely) without the use of GPS information.
